Improved approximation of interactive dynamic influence diagrams using discriminative model updates
Improved approximation of interactive dynamic influence diagrams using discriminative model updates
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DOI：
10.1145/1558109.1558138
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发表时间：
2009-05
期刊：
影响因子：
3.7
通讯作者：
Prashant Doshi;Yi-feng Zeng
中科院分区：
文献类型：
--
作者：
Prashant Doshi;Yi-feng Zeng
交互式动态影响图（I-DID）是在不确定环境中由其他代理共享的顺序决策的图形模型。随着时间的推移，用于解决I-DID的算法面临着指数增长的归因于其他代理的候选模型空间的挑战。我们正式的最小模型集的概念，这有利于不同的近似技术之间的定性比较。然后，我们提出了一种新的近似技术，最大限度地减少候选模型的空间，通过区分模型更新。我们的经验证明，我们的方法比之前基于聚类的近似技术在性能上显着提高。
Interactive dynamic influence diagrams (I-DIDs) are graphical models for sequential decision making in uncertain settings shared by other agents. Algorithms for solving I-DIDs face the challenge of an exponentially growing space of candidate models ascribed to other agents, over time. We formalize the concept of a minimal model set, which facilitates qualitative comparisons between different approximation techniques. We then present a new approximation technique that minimizes the space of candidate models by discriminating between model updates. We empirically demonstrate that our approach improves significantly in performance on the previous clustering based approximation technique.